Chronic illness can be overwhelming to patients because it impacts many areas of their daily lives. Accordingly, many patients turn to online health support groups to get social support. In face-to-face patient support groups (F2F), health professional moderators provide clinical expertise within the context of peer-patients' sharing of experience. However, in online health community settings, because health professionals' time and resources are expensive, it is challenging to get health professionals' opinions for thousands of messages posted each day. To solve this problem, I propose to develop methods and techniques that maximize the use of already available clinical expertise online for online peer-patient conversation threads by developing a system, InfoMediator. The InfoMediator will semi-automatically weave health professionals' existing answers to patients' questions into peer-patient conversations by using Natural Language Processing (NLP) techniques complemented by user feedback. As the career development component of this proposal, I deepen my skills and knowledge in NLP necessary for proposed work and patient education. As I develop the NLP techniques and knowledge in patient education, I and my mentors will develop methods and techniques that address weaving clinical expertise within peer-patient conversations by designing, implementing, and evaluating socio-technical aspects of the InfoMediator. The training opportunities provided by this NIH NLM K01 grant, together with the supportive research environment at Michigan State University, will help further extend my existing expertise in human-computer interaction, design, and health informatics to establish my independent informatics research program in patient-centered technologies. Focusing on persons with diabetes, the outcomes of the proposed research will help us understand how to empower persons with diabetes to improve self- efficacy and self-care, while increasing the quality of online health information environment.